Table 3. 1 Breakpoints of different pollutants in IND-AQI (CPCB, 2014)
AQI Category (Range) PM2.5 24-hr NO2 24-hr O3 8-hr CO 8-hr SO2 24-hr
Good (0-50) 0-30 0-40 0-50 0-1.0 0-40
Satisfactory (51-100) 31-60 41-80 51-100 1.1-2.0 41-80 Moderate (101-200) 61-90 81-180 101-168 2.1-10 81-380
Poor (201-300) 91-120 181-280 169-208 10.1-17 381-800 Very Poor (301-400) 121-250 281-400 209-748 17.1-34 801-1600
Severe (401-500) 250+ 400+ 748+ 34+ 1600+
Note: While CO concentrations are expressed in mg/m-3; the other pollutants are expressed in Β΅g m-3.
The expected number of premature deaths (PDi) due to a pollutant exposure can be calculated by equation 3.4, using mortality rate (M) and total population (Pop).
PDi = M ΓRRiβ 1
RRi Γ πππ (3.4)
The relative risk (RRi), in equation 3.4, was calculated by equation 3.2, using ci as the averaged PM2.5 concentration during the analysis period, and the ccf as the targeted PM2.5 limit.
requires health data, which might not be available to public in many countries. In such cases to study the health risks in countries like India where health data is not available we can make use of risk functions. These functions are based upon previous health studies, which are compiled to obtain the relationship between pollutant concentration and health risk. These functions are specific to a pollutant so risk functions of PM2.5 cannot be used to estimate health risk due to exposure to PM10 concentration or vice versa. The Risk functions (IER) are based on assumption that risk will depend on mass of particulate matter, mass of heavier particulate matter such as PM10
are more prone to deposition (wet or dry) as compared to lighter particulate matter particles such as PM2.5 and hence will have lesser effect on health as compared to PM2.5 and thus will follow different curve.
3.2.1 Assumptions
ο§ The RRβs calculated are a function of inhaled PM2.5 mass from AAP, AS, SHS and HAP and the toxicity depends on the mass of PM2.5 inhaled since studies regarding health effects of components of particulate matter havenβt been conducted done yet and hence cannot be evaluated (R. T. Burnett et al., 2014).
ο§ The RR of mortality is a function of long term cumulative exposure and does not depend on temporal pattern of exposure (R. T. Burnett et al., 2014) .
ο§ The RR due to each type of exposure is independent of other types of exposure (R. T.
Burnett et al., 2014).
ο§ Single value baseline mortality rate for India from WHO was assumed to be representative of all states.
3.2.1 Linearly increasing function
A. J. Cohen et al. (2005) developed a risk function to estimate global burden of disease in 14 WHO regions based on mortality from cardiopulmonary causes, lung cancer and acute respiratory infections in children from 0 to 5 years. However, the studies used for developing the function were based on risk coefficients from (Pope et al., 2002) for adults and a meta-analysis summary of five time-series studies of mortality in case of children. As no studies from developing countries were included, it resulted in large variations in burden estimates and hence could not be extended to smaller geographical regions. Burden estimates were evaluated based on assumptions that health risk varies linearly between concentrations of 7.5 and 50 Β΅g/m3 and thereafter remains constant.
3.2.2 Power function
C. A. Pope, 3rd et al. (2009) used the relative risks for ambient air pollution and cigarette smoking from American Cancer Society (ACS) cohorts, and Second hand smoke (SHS) estimates from other cohort studiesto postulate a risk function for cardiovascular mortality. However, different measures of exposure were used in different studies. i.e. exposure in ambient air pollution studies and cigarette smoking are measured in Β΅g/m3 and number of cigarettes smoked per day, respectively. For calculation purposes, average daily dose was used as the common metric. The average daily dose inhaled is measured by multiplying PM2.5 concentration with average inhalation rate. The average inhalation rate for the study was taken as 18 m3/day, and average PM2.5 dose inhaled per cigarette was taken as 12 mg. In case of SHS exposure, for low to moderate and moderate to high exposure, the average PM2.5 was estimated to be 20 Β΅g/m3 and 50 Β΅g/m3, respectively. Curve fitting revealed non-linear power function to be the best fit for all the estimates (equation 3.5).
π π = 1 + 0.2968(πππ π)0.2107 (3.5) C. A. Pope et al. (2011) fitted a curve for estimating lung cancer risk due to PM2.5 concentration to understand whether the risks of lung cancer follows similar trend as cardiovascular diseases. As discussed above, previous estimates considered cardiovascular and lung cancer mortality to flatten out at 50 Β΅g/m3 (A. J. Cohen et al., 2005). If the lung cancer risk estimates do not flatten at high PM2.5 concentrations similar to cardiovascular diseases, the risk due to lung cancer will be severely underestimated in areas with high PM2.5 concentrations. American Cancer Society (ACS)-PM2.5
cohort was the source of risk estimates from active cigarette smoking. Risk estimates of ambient PM2.5 concentration were obtained from ACS CPS-II cohort (C. A. Pope, 3rd et al., 2002; C. A.
Pope, 3rd et al., 2004; C. A. Pope, 3rd et al., 1995), Harvard six cities analyses (Dockery et al., 1993; Laden et al., 2006) and Womenβs health initiative study (Miller et al., 2007) while SHS estimates were obtained from 2006 Surgeon Generalβs Report (Health & Services, 2006) and INTERHEART study (Teo et al., 2006). Lung cancer exposure-response plot indicated a monotonic increasing risk estimate with increasing PM2.5 concentrations as given in equation (3.6).
π π = 1 + 0.3195(πππ π)0.7433 (3.6)
The equations 3.5 and 3.6 can be expressed commonly as equation (3.7)
π π = 1 +ο‘(πππ π)ο’ (3.7)
Recently, Chowdhury and Dey (2016) fitted separate curves for COPD, IHD, Lung Cancer, Stroke and acute lower respiratory infection (ALRI) using equation (3.7) with risk estimates from 141
different cohort studies across different regions and exposures from ambient PM2.5 concentration, SHS, active smoking (AS) and household air pollution (HAP). The ο‘ and ο’ values are computed for each of the four disease based on the risk estimates as obtained from previous studies.
3.2.3 Integrated exposure response function (IER)
R. T. Burnett et al. (2014) developed an integrated risk function using Relative Risk (RR) from epidemiological studies due to exposure from ambient air pollution (AAP), SHS, AS and HAP.
The risk estimates for AAP was obtained from cohort studies conducted across the globe, SHS and AS risk estimates were obtained as discussed by C. A. Pope et al. (2011) and risk estimates for HAP were provided by (Smith et al., 2014). Since the estimates from different types of exposure are different, they were converted to a common metric (C. A. Pope, 3rd et al., 2009; C. A. Pope et al., 2011) as explained in section above. Single cigarette was observed to be equivalent to exposure to a daily ambient PM2.5 concentration of 667 Β΅g/m3 and the same information was used to convert no of cigarettes smoked into daily ambient PM2.5 concentration in this study.
The function as provided in equation (2.1) was based on information from previous studies, which indicated that risk estimates due to cardiovascular diseases initially increases swiftly and flattens out at higher PM2.5 concentration (C. A. Pope, 3rd et al., 2009), while in case of lung cancer the risk estimates increases monotonically with increase in PM2.5 concentration (C. A. Pope et al., 2011).
For c<ccf
π π πΌπΈπ (π) = 1
For c>=ccf
π π πΌπΈπ (π§) = 1 + πΌ{1 β exp [βπΎ(π β πππ)πΏ]
c=exposure to PM2.5 in Β΅g/m3
ccf=concentration below which the risk is assumed to be zero
The unknown parameters (Ξ±, Ξ³, Ξ΄) are obtained by nonlinear regression methods.
3.3 GEOGRAPHICAL SOURCE REGION CONTRIBUTION TO MORTALITY IN